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programming in C/C++, preferably also Rust, and of POSIX, the Linux/Unix kernel or RTOS. Documented competence in parallel and distributed systems, including GPU programming (e.g. CUDA). Ability to explain
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translational) centers and laboratories, including: The National Center for Supercomputing Applications housing the most performant GPU-based systems and expertise in high-performance computing, the Advanced
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, AI agent orchestration platforms and GPU-enabled AI infrastructure. Responsibilities: Platform operations and reliability Own day-to-day operations of SEA-LION API Farm, our multi-cloud LLM inference
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monitoring plugin. Based in the PRISMA Computer Vision lab within the School of Computer Science at the University of Sheffield, the post offers access to high-performance computing and GPU facilities
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performance GPU computing — whether through thousands of commercial GPUs or a handful of Nvidia NVL72 racks — and specialized signal processing hardware. Automation: Develop and implement automated processes
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systems and underwater imaging. 4. Linux system administration and scientific computing workflows, including environment management, GPU-accelerated workloads, and remote/headless operation. 5. Familiarity
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PID2024-155476OB-I00 (internal code J-03493), funded by the State Research Agency. Fuctions to be developed: Expand and improve GPU evaluation tools. Characterize existing GPU architectures in terms
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, networking, and GPU utilization. Help maintain reproducible training recipes, configuration files, launch scripts, and documentation. Work with researchers and CSCS engineers to improve the reliability and
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infrastructure, making our environment highly heterogeneous. This means we get to work with a range of hardware, including the latest GPUs (e.g., B200, RTX 6000 Blackwell Pro, H200) and CPU architectures
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functioning in a dynamic technological environment. Preferred Qualifications: Experience with parallel computing, GPU operation (CUDA Toolkit), multi-GPU training, and distributed frameworks for machine